Skip Navigation

European Journal of Heart Failure 2005 7(2):261-267; doi:10.1016/j.ejheart.2004.05.011
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Jünger, J.
Right arrow Articles by Haass, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jünger, J.
Right arrow Articles by Haass, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2005 European Society of Cardiology

Depression increasingly predicts mortality in the course of congestive heart failure

Jana Jüngera,*, Dieter Schellberga, Thomas Müller-Tascha, Georg Rauppa, Christian Zugckb, Armin Haunstetterb, Stephan Zipfela, Wolfgang Herzoga and Markus Haassb,c

a Department of General Internal and Psychosomatic Medicine, University of Heidelberg INF 410, D-69120 Heidelberg, Germany
b Department of Cardiology, University of Heidelberg Heidelberg, Germany
c Department of Cardiology, Theresienkrankenhaus Mannheim, Germany

* Corresponding author. Tel.: +49 6221 568657; fax: +49 6221 565749. E-mail address: Jana_Juenger{at}med.uni-heidelberg.de


   Abstract
Top
 Abstract
1. Introduction
 2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
 7. Results
 8. Discussion
 9. Conclusions
 References
 
Background: Congestive heart failure (CHF) is frequently associated with depression. However, the impact of depression on prognosis has not yet been sufficiently established.

Aims: To prospectively investigate the influence of depression on mortality in patients with CHF.

Methods: In 209 CHF patients depression was assessed by the Hospital Anxiety and Depression Scale (HADS-D).

Results: Compared to survivors (n=164), non-survivors (n=45) were characterized by a higher New York Heart Association (NYHA) functional class (2.8±0.7 vs. 2.5±0.6), and a lower left ventricular ejection fraction (LVEF) (18±8 vs. 23±10%) and peakVO2 (13.1±4.5 vs. 15.4±5.2 ml/kg/min) at baseline. Furthermore, non-survivors had a higher depression score (7.5±4.0 vs. 6.1±4.3) (all P<0.05). After a mean follow-up of 24.8 months the depression score was identified as a significant indicator of mortality (P<0.01). In multivariate analysis the depression score predicted mortality independent from NYHA functional class, LVEF and peakVO2. Combination of depression score, LVEF and peakVO2 allowed for a better risk stratification than combination of LVEF and peakVO2 alone. The risk ratio for mortality in patients with an elevated depression score (i.e. above the median) rose over time to 8.2 after 30 months (CI 2.62–25.84).

Conclusions: The depression score predicts mortality independent of somatic parameters in CHF patients not treated for depression. Its prognostic power increases over time and should, thus, be accounted for in risk stratification and therapy.

Key Words: CHF, Congestive heart failure • LVEF, Left ventricular ejection fraction • NYHA, New York Heart Association • PeakVO2, peak oxygen uptake in cardiopulmonary exercise testing • HADS-D, Hospital Anxiety and Depression Scale in German

Received August 8, 2003; Revised May 5, 2004; Accepted May 24, 2004


   1. Introduction
Top
 Abstract
 1. Introduction
2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
 7. Results
 8. Discussion
 9. Conclusions
 References
 
Despite advances in the treatment of congestive heart failure (CHF), this epidemic continues unabated worldwide [1,2]. The high morbidity and mortality as well as the marked decrease in quality of life [3] associated with CHF points to the need for increased attention towards additional risk factors for the course of CHF. While depression has been well-established as an independent risk factor for development of coronary artery disease [4] and as an independent predictor for its prognosis [5–7], only recently have the unfavorable effects that depression exerts on CHF been reported. Until the studies of Abramson et al. [8] and Williams et al. [9], it was not well understood that depression is a risk factor for predisposition to CHF. The few studies on the prognostic value of depression in CHF patients have shown inconsistent results. Two studies with an observation time of 6–12 months found no predictive value of depression after multivariate controlling for somatic parameters [10,11], while another showed a predictive value of depression after 24 months [12]. However, none of these studies controlled for the established predictors, peakVO2 and left ventricular ejection fraction (LVEF), simultaneously. For the latter two a highly predictive value concerning mortality was shown recently in a simple two-variable model [13].

The first aim of the present study was to test the hypothesis that depression independently predicts mortality in patients with CHF, even after adjusting for established clinical risk variables and concomitant beta-blocking medication and to show that entering information on depression score into a model comprising peakVO2 and LVEF would increase the overall risk stratification. The second aim was to measure the interaction of the predictive effect of depression with time.


   2. Methods
Top
 Abstract
 1. Introduction
 2. Methods
3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
 7. Results
 8. Discussion
 9. Conclusions
 References
 
Two hundred and nine patients with stable CHF (New York Heart Association (NYHA) functional classes I–III) were prospectively enrolled into the study through the Department of Cardiology at the University of Heidelberg between March 1996 and March 1999, after giving written informed consent. The study complied with the Declaration of Helsinki and was approved by the Ethics Committee for human research of the University of Heidelberg.

The cardiac diagnosis was based on left heart catheterization and coronary angiograms prior to enrollment. The major inclusion criterion was LVEF ≤45%, as determined by radionuclide ventriculography [13,14]. Patients in NYHA functional class IV were excluded, as were those who had neurological, orthopedic, peripheral vascular or severe pulmonary diseases, which may have impaired successful completion of exercise testing (i.e. determination of peakVO2 by cardiopulmonary exercise testing) [13]. Patients who were not able to speak German fluently were also excluded.

NYHA functional class was determined by an independent investigator prior to assessment of somatic variables. Furthermore, the investigator who applied the standard Hospital Anxiety and Depression Scale in German (HADS-D) questionnaires was blinded to the aforementioned data. All parameters were collected within a period of 48 h.


   3. Depression and anxiety
Top
 Abstract
 1. Introduction
 2. Methods
 3. Depression and anxiety
4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
 7. Results
 8. Discussion
 9. Conclusions
 References
 
Patients' self assessment of depression and anxiety were determined at baseline by the German version of the Hospital Anxiety and Depression Scale, the HADS-D [15,16], a validated and reliable 14 item checklist (7 items for depression and 7 items for anxiety) well-established among cardiac patients [15]. It has one scale-score for depression and one for anxiety. Patients with ≥8 points on the depression scale are suspected of having depression and with ≥10 points of having anxiety [15,16]. As there is no validated prognostic cutoff point, the median of the HADS-D depression score was used for survival-analysis. For analysis of risk stratification, the continuous variable was used.


   4. Predictor and outcome variable
Top
 Abstract
 1. Introduction
 2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
5. Endpoint monitoring
 6. Statistical analysis
 7. Results
 8. Discussion
 9. Conclusions
 References
 
The primary independent, or predictor, variable of interest was the depression score from the baseline assessment. The primary endpoint was all-cause mortality, defined as death due to any cause. The dependent variable was time from baseline to the endpoint (death) or time until transplantation or termination of the study. In this context the status of censored has to be defined. All patients undergoing cardiac transplantation were considered as survivors until the date of the transplantation.


   5. Endpoint monitoring
Top
 Abstract
 1. Introduction
 2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
6. Statistical analysis
 7. Results
 8. Discussion
 9. Conclusions
 References
 
We obtained endpoint information during regular outpatient visits or by telephone calls to the patients' home or their family physician. Information on endpoint status was available for all patients at the pre-established time point (i.e. July 2001). Depression was only assessed at baseline. Mean observation time was 24.8 months (range: minimum 0.6 to maximum 36 months). The predefined endpoint was all-cause mortality. Death without transplantation was defined as an outcome event. All patients undergoing cardiac transplantation were considered as survivors until the date of their transplantation regardless of the postoperative outcome.


   6. Statistical analysis
Top
 Abstract
 1. Introduction
 2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
7. Results
 8. Discussion
 9. Conclusions
 References
 
Statistical analysis was performed with standard software (SAS version 6.09). Spearman rank correlation coefficient was used as a measure of association between variables. A nonparametric two-sample Wilcoxon test was used to test for differences between groups (survivors vs. non-survivors). Survival curves were calculated using the Kaplan–Meier method [17]. Multivariate Cox proportional hazards analysis was used to identify the most important predictors of mortality [18]. The log of the negative log survival curves stratified according to the HADS-D depression median was used to evaluate the basic proportional hazards assumption of the Cox-model for depression. This assumption was violated, as the curves did not run parallel (not shown). Standardized Schoenfeld-smoothed residuals were analysed to demonstrate the functional form of interaction between depression and its effect on mortality over time, as proposed by Grambsch and Therneau [19]. Receiver-operating characteristic curves were constructed by means of plotting true-positive rates (sensitivity) against false-positive rates (1-specificity) [20,21]. The data are expressed as mean±standard deviation (S.D.). A P-value <0.05 was considered significant.


   7. Results
Top
 Abstract
 1. Introduction
 2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
 7. Results
8. Discussion
 9. Conclusions
 References
 
7.1. Clinical correlation
All results are from Spearman rank correlations. Demographic data and functional parameters of all CHF patients at baseline are summarized in Table 1. The majority of the patients (88.5%) were in NYHA functional-classes II or III. The advanced stage of disease was also reflected by the fact that 46% of the patients had a peakVO2 ≤14 ml/kg/min and 52% had a LVEF of ≤ 20%. Standard medical treatment is listed in Table 1. No patient received antidepressant medication.


View this table:
[in this window]
[in a new window]

  		Table 1 Baseline clinical, functional and psychosocial characteristics of the patient sample

 
HADS-D depression score and anxiety score increased, while LVEF and peakVO2 decreased with NYHA functional class. The total CHF sample was characterized by significantly increased HADS-D depression score and anxiety scores compared to a healthy reference group [22] (data not shown).

The HADS-D depression score was just correlated with LVEF (r=–0.134; P=0.05) and was only weakly correlated with peakVO2 (r=–0.28; P<0.01). However, a closer relationship was observed between depression score and NYHA functional class (r=0.40; P<0.01) and anxiety (r=0.68; P<0.01).

7.2. Survival analysis
Results are from the nonparametric Wilcoxon two-sample test. During a mean follow-up of 24.8 months 45 patients died, all due to cardiac causes. Compared to survivors, non-survivors were characterized by a higher NYHA functional class (2.8±0.7 vs. 2.5±0.6) and a lower LVEF (18±8 vs. 23±10%) and peakVO2 (13.1±4.5 vs. 15.4±5.2 ml/kg/min) (all P<0.05). Depression score was significantly higher among non-survivors (7.5±4.0 vs. 6.1±4.3, P<0.05). Anxiety score among these patients was also marginally higher but the difference was not statistically significant (7.8±3.6 vs. 6.7±4.1, P=0.103). Depression score and anxiety score increased concomitantly with rising NYHA functional class (Table 1).

7.3. Univariate and multivariate analysis
In univariate analysis depression score emerged—next to NYHA functional class (data not shown), LVEF and peakVO2—as a significant (P<0.01) prognostic parameter (Table 2). Kaplan–Meier survival curves of depression, LVEF and peakVO2 divided according to the cutoff-values (median) are shown in Fig. 1a, b and c. Multivariate Cox proportional hazard analysis revealed that the depression score predicted prognosis independent of LVEF and peakVO2 (Table 2). The combination of depression score, peakVO2 and LVEF allowed a better risk stratification than the combination of the established parameters LVEF and peakVO2 alone (Chi-square 24.93 vs. 20.24, P<0.001). Controlling for medication (ACE-inhibitors, diuretics, digitalis and beta-blockers) and etiolgy (ischaemic vs. dilated cardiomyopathy) in a multivariable Cox regression model, the effects of LVEF, peakVO2 and HADS depression score remained significant and unaltered in size. None of the former variables controlled for turned out as significant.


View this table:
[in this window]
[in a new window]

  		Table 2 Univariate and multivariate Cox regression analysis of study variables and survival

 


Figure 1
View larger version (9K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 1 Kaplan–Meier survival curves of the total study population divided according to cutoff-values (median). (a) HADS-D depression score, cutoff 6 points; Log rank chi-square 11.20, P=0.0008. (b) LVEF, cutoff 20%; Log-rank chi-square 8.08, P=0.0045. (c) PeakVO2, cutoff 14 ml/min/kg; Log rank chi-square 6.90, P=0.0086.

 
7.4. Receiver-operating characteristic curves for survival at 36 months
After maximum follow-up of 36 months, parameters identified as independent predictors by multivariate Cox regression analysis were entered into receiver operating characteristic analysis: By adding depression score to the combination of LVEF and peakVO2, risk prediction improved significantly (Fig. 2).


Figure 2
View larger version (11K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 2 Receiver-operating characteristic curves of prediction of survival at 36 months. The combination of HADS-D depression score, LVEF and peakVO2 (AUC, 0.831) yields significantly more prognostic information than combination of LVEF and peakVO2 alone (AUC, 0.774). n=45 patients died within 36 months, n=164 survived; area-test one-tailed P<0.0028.

 
7.5. Analysis of the time-dependent mortality-risk of depression
As shown in Table 3, mortality-risk associated with depression score rises over time. When calculating the hazard ratios over time, it becomes clear that depression exerts almost no risk in the first year of follow-up and rises to an 8-fold level after 30 months (Table 3).


View this table:
[in this window]
[in a new window]

  		Table 3 Time-dependent risk ratios for mortality for patients with a HADS-D depression score >6

 

   8. Discussion
Top
 Abstract
 1. Introduction
 2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
 7. Results
 8. Discussion
9. Conclusions
 References
 
In this study, depression was an important independent risk factor for mortality of CHF patients. In multivariate analysis, depression score predicted prognosis independent of NYHA functional class, LVEF and peakVO2, as well as etiology and concomitant medication. The combination of depression score, LVEF and peakVO2 allowed for a better risk stratification than the combination of the established parameters LVEF and peakVO2 alone.

In previous studies the investigators focused primarily on the impact of depression on prognosis without controlling simultaneously for the established prognostic parameters LVEF and peakVO2 [10–12]. The studies showed a predictive value of depression for combined endpoints such as mortality and re-hospitalization [10] or death and functional decline [11]. However, after controlling for multiple variables (such as NYHA functional class, LVEF, age) and focusing on the single endpoint mortality the results lost their statistic significance [10,11]. In the more recent studies of Jiang et al. [10] and Vaccarino et al. [11] the observation time of CHF patients was restricted to 1 year. Also, Koenig [23] found no prognostic value of depression after an observation time of 1 year. Faris et al. [24] showed reduced survival of patients with non-ischaemic heart failure and depression over a long observation period of 5 years. However, peakVO2 was available only in 37% of the study population and the diagnosis of depression was based on a clinical diagnosis through review of patients' medical records, not on an established instrument or questionnaire.

In the present study, patients were followed over a mean of 24.8 months. The main effect of depression on mortality began at the end of the first year of observation. This time-dependent development of the prognostic effect of depression in patients with CHF was shown here for the first time. In a previous study we were able to show that patients with an endstage heart failure and an additional increased preoperative depression score showed an unfavorable outcome after heart transplantation, particularly in the long-term course [25]. Denollet and Brutsaert [26] state an odds ratio for cardiac death of 7.5 after myocardial infarction with a reduced LVEF in patients with negative affectivity, which is part of depressive pathology, after the long observation period of 6 to 10 years. So, both studies show depression as a predictor of mortality in the long run. Abramson et al. [8] found that depression independently predicts the onset of CHF in patients with isolated systolic hypertension over an observation period of 4.5 years. Finally, a study by Fredman et al. [27] showed an association of depression with mortality in elderly women that was much stronger after 6 years than after just 2 years. Thus the effect of depression on mortality seems to develop over time and might only be recognized in long-term observation.

There are several mechanisms that could account for the association between depression and increased heart failure mortality. In patients with depression, hypercortisolism and an activation of the sympathetic nervous system can be observed [28], as well as elevated levels of cytokines [29], which in turn are predictive factors for CHF mortality [30,31]. Also, patients with depression show reduced heart rate variability [32]. Higher platelet activity and consecutively an increased risk for cardiovascular events has also been shown in some studies [4,33]. However, the present study found no significant difference in the prognostic effect of depression regarding ischemic or non-ischemic etiology of heart failure. Likewise, Abramson et al. [8] found that depression retained its predictive power for the risk of developing CHF even after controlling for a history of myocardial infarction.

In addition to these factors, psychological explanations linking depression and increased mortality in heart failure are being discussed. Patients with depression are known to have poor social contacts and support networks [25,34]. Additionally, they show a reduced compliance with treatment because of an appreciable degree of hopelessness, withdrawal from the social network, with a subsequent loss of emotional support and possible reductions in the cognitive functioning essential for following treatment recommendations [35]. All the above mentioned factors might increase the risk of non-compliance with medication regimens and thus increase the risk of cardiac decompensation [35,36].

8.1. Limitations of the study
This study was performed at a tertiary referral center. Thus the current CHF sample does not represent the typical CHF population seen by a general practitioner. The majority of CHF-patients in a general practitioner's practice are characterized by a smaller impairment in LVEF, a higher prevalence of coronary artery disease, and an older age [37]. Also, there was a very high proportion of male patients in this sample. The clinical diagnosis of depressive disorder cannot be made with the HADS-D. However, the HADS-D has been shown to be a reliable and valid screening instrument for depressive symptoms in general internal patients [15]. Additionally, it seemed important to select a simple, clinically applicable instrument that could be completed by the patients themselves and that could easily be used for screening and follow-up in other centers.

Since the analysis of the prognostic impact of the HADS-D was restricted to a single timepoint (baseline), it cannot be ruled out that the depression score further increased especially in those patients with a depression score above the median at baseline. Furthermore, as the patients were not being treated for depression, the present study allows no conclusion on the prognostic impact of specifically coping with depression.


   9. Conclusions
Top
 Abstract
 1. Introduction
 2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
 7. Results
 8. Discussion
 9. Conclusions
References
 
The present findings are of major clinical importance. Screening for depression is a simple, well-established, non-invasive method that a patient can self-administer. The HADS-D offers the physician not only information about the psychological status of a patient with CHF, but also prognostic information that is independent of somatic parameters.

While at the physical level excellent pharmacological treatments are available to improve prognosis, a diagnosis of depression is also a state of impaired health that needs to be treated by pharmacological and/or psychotherapeutic means. As the negative effect of depression on prognosis seems to evolve slowly, there may be sufficient time to initiate this treatment. If these findings are confirmed by future studies, the influence on risk stratification for these patients should be investigated.


   Acknowledgements
 
This study was supported by grants from the faculty for clinical medicine of the University of Heidelberg (projects 32/95 and 158/97).


   References
Top
 Abstract
 1. Introduction
 2. Methods
 3. Depression and anxiety
 4. Predictor and outcome...
 5. Endpoint monitoring
 6. Statistical analysis
 7. Results
 8. Discussion
 9. Conclusions
 References
 

  1. Ho K.K., Pinsky J.L., Kannel W.B., Levy D. The epidemiology of heart failure: the Framingham Study. J. Am. Coll. Cardiol. (1993) 22:6A–13A.[Medline]
  2. O'Connell J.B. The economic burden of heart failure. Clin. Cardiol. (2000) 23(III):6–10.[Web of Science]
  3. Juenger J., Schellberg D., Kraemer D., et al. Health related quality of life in patients with congestive heart failure: comparison with other chronic diseases and relation to functional variables. Heart (2002) 87:235–241.[Abstract/Free Full Text]
  4. Rozanski A., Blumenthal J.A., Kaplan J. Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. Circulation (1999) 99:2192–2217.[Abstract/Free Full Text]
  5. Ariyo A.A., Haan M., Tangen C.M., et al. Depressive symptoms and risks of coronary heart disease and mortality in elderly Americans Cardiovascular Health Study Collaborative Research Group. Circulation (2000) 102:1773–1779.[Abstract/Free Full Text]
  6. Barefoot J.C., Helms M.J., Mark D.B., et al. Depression and long-term mortality risk in patients with coronary artery disease. Am. J. Cardiol. (1996) 78:613–617.[CrossRef][Web of Science][Medline]
  7. Frasure-Smith N., Lesperance F. Coronary artery disease, depression and social support only the beginning. Eur. Heart J. (2000) 21:1043–1045.[Free Full Text]
  8. Abramson J., Berger A.C., Krumholz H.M., Vaccarino V. Depression and risk of heart failure among older persons with isolated systolic hypertension. Arch. Intern. Med. (2001) 161:1725–1730.[Abstract/Free Full Text]
  9. Williams S.A., Kasl S.V., Heiat A., Abramson J.L., Krumholz H.M., Vaccarino V. Depression and risk of heart failure among the elderly: a prospective community-based study. Psychosom. Med. (2002) 64:6–12.[Abstract/Free Full Text]
  10. Jiang W., Alexander J., Christopher E., et al. Relationship of depression to increased risk of mortality and rehospitalization in patients with congestive heart failure. Arch. Intern. Med. (2001) 161:1849–1856.[Abstract/Free Full Text]
  11. Vaccarino V., Kasl S.V., Abramson J., Krumholz H.M. Depressive symptoms and risk of functional decline and death in patients with heart failure. J. Am. Coll. Cardiol. (2001) 38:199–205.[Abstract/Free Full Text]
  12. Murberg T.A., Bru E., Svebak S., Tveteras R., Aarsland T. Depressed mood and subjective health symptoms as predictors of mortality in patients with congestive heart failure: a two-year follow-up study. Int. J. Psychiatry Med. (1999) 29:311–326.[CrossRef][Web of Science][Medline]
  13. Zugck C., Krüger C., Kell R., et al. Risk stratification in middle-aged patients with congestive heart failure: prospective comparison of the Heart Failure Survival Score (HFSS) and a simplified two-variable model. Eur. J. Heart Fail. (2001) 3:577–585.[Abstract/Free Full Text]
  14. Meyer F.J., Borst M.M., Zugck C., et al. Respiratory muscle dysfunction in congestive heart failure: clinical correlation and prognostic significance. Circulation (2001) 103:2153–2158.[Abstract/Free Full Text]
  15. Herrmann C. International experiences with the Hospital Anxiety and Depression Scale—a review of validation data and clinical results. J. Psychosom. Res. (1997) 42:17–41.[CrossRef][Web of Science][Medline]
  16. Bjelland I., Dahl A.A., Haug T.T., Neckelmann D. The validity of the Hospital Anxiety and Depression Scale. An updated literature review. J. Psychosom. Res. (2002) 52:69–77.[CrossRef][Web of Science][Medline]
  17. Kaplan E.L., Meier P. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. (1958) 53:457–481.[CrossRef][Web of Science]
  18. Cox D.R. Regression Models and Life-Tables. J.R. Stat. Soc. (1972) Series B 34:187–202.
  19. Grambsch P., Therneau T. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika (1994) 81(3):515–526.[Abstract/Free Full Text]
  20. Metz C.E., Wang P., Kronman H. A new approach for testing the significance of differences between ROC curves measured from correlated data. In: Information processing in medical imaging—Deconinck F., ed. (1984) The Hague: Martinus Nijhoff. 432–445.
  21. Metz C.E., Herman B.A., Roe C.A. Statistical comparison of two ROC-curve estimates obtained from partially-paired datasets. Med. Decis. Making (1998) 18:110–121.[Abstract/Free Full Text]
  22. Hinz A., Schwarz R. Anxiety and depression in the general population: normal values in the Hospital Anxiety and Depression Scale. Psychother. Psychosom. Med. Psychol. (2001) 51:193–200.[CrossRef][Web of Science][Medline]
  23. Koenig H.G. Depression in hospitalized older patients with congestive heart failure. Gen. Hosp. Psychiatry (1998) 20:29–43.[CrossRef][Web of Science][Medline]
  24. Faris R., Purcell H., Henein M.Y., Coats A.J. Clinical depression is common and significantly associated with reduced survival in patients with non-ischaemic heart failure. Eur. J. Heart Fail. (2002) 4:541–551.[Abstract/Free Full Text]
  25. Zipfel S., Schneider A., Wild B., et al. Effect of depressive symptoms on survival after heart transplantation. Psychosom. Med. (2002) 64(5):740–747.[Abstract/Free Full Text]
  26. Denollet J., Brutsaert D.L. Personality, disease severity, and the risk of long-term cardiac events in patients with a decreased ejection fraction after myocardial infarction. Circulation (1998) 97:167–173.[Abstract/Free Full Text]
  27. Fredman L., Magaziner J., Hebel J.R., Hawkes W., Zimmerman S.I. Depressive symptoms and 6-year mortality among elderly community-dwelling women. Epidemiology (1999) 10:54–59.[CrossRef][Web of Science][Medline]
  28. Musselman D.L., Evans D.L., Nemeroff C.B. The relationship of depression to cardiovascular disease: epidemiology, biology, and treatment. Arch. Gen. Psychiatry (1998) 55:580–592.[Abstract/Free Full Text]
  29. Gold P.W., Chrousos G.P. The endocrinology of melancholic and atypical depression: relation to neurocircuitry and somatic consequences. Proc. Assoc. Am. Physicians (1999) 111:22–34.[CrossRef][Web of Science][Medline]
  30. Cohn J.N., Johnson G.R., Shabetai R., et al. Ejection fraction, peak exercise oxygen consumption, cardiothoracic ratio, ventricular arrhythmias, and plasma norepinephrine as determinants of prognosis in heart failure. The V-HeFT VA Cooperative Studies Group. Circulation (1993) 87:VI5–V16.[Medline]
  31. Kell R., Haunstetter A., Dengler T.J., Zugck C., Kübler W., Haass M. Do cytokines enable risk stratification to be improved in NYHA functional class III patients? Comparison with other potential predictors of prognosis. Eur. Heart J. (2002) 23:70–78.[Abstract/Free Full Text]
  32. Gorman J.M., Sloan R.P. Heart rate variability in depressive and anxiety disorders. Am. Heart J. (2000) 140:S77–S83.[CrossRef]
  33. Musselman D.L., Tomer A., Manatunga A.K., et al. Exaggerated platelet reactivity in major depression. Am. J. Psychiatry (1996) 153:1313–1317.[Abstract/Free Full Text]
  34. Dracup K., Walden J.A., Stevenson L.W., Brecht M.L. Quality of life in patients with advanced heart failure. J. Heart Lung Transplant. (1992) 11:273–279.[Web of Science][Medline]
  35. DiMatteo M.R., Lepper H.S., Croghan T.W. Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence. Arch. Intern. Med. (2000) 160:2101–2107.[Abstract/Free Full Text]
  36. Michalsen A., Konig G., Thimme W. Preventable causative factors leading to hospital admission with decompensated heart failure. Heart (1998) 80:437–441.[Abstract/Free Full Text]
  37. Cowie M.R., Mosterd A., Wood D.A., et al. The epidemiology of heart failure. Eur. Heart J. (1997) 18:208–225.[Free Full Text]

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Eur J Heart FailHome page
I. Lesman-Leegte, D. J. van Veldhuisen, H. L. Hillege, D. Moser, R. Sanderman, and T. Jaarsma
Depressive symptoms and outcomes in patients with heart failure: data from the COACH study
Eur J Heart Fail, December 1, 2009; 11(12): 1202 - 1207.
[Abstract] [Full Text] [PDF]

Home page
 Eur J Heart FailHome page
H. Faller, S. Stork, M. Schowalter, T. Steinbuchel, V. Wollner, G. Ertl, and C. E. Angermann
Depression and survival in chronic heart failure: Does gender play a role?
Eur J Heart Fail, October 1, 2007; 9(10): 1018 - 1023.
[Abstract] [Full Text] [PDF]

Home page
 Eur J Heart FailHome page
I. Lesman-Leegte, T. Jaarsma, R. Sanderman, G. Linssen, and D. J. van Veldhuisen
Depressive symptoms are prominent among elderly hospitalised heart failure patients
Eur J Heart Fail, October 1, 2006; 8(6): 634 - 640.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Jünger, J.
Right arrow Articles by Haass, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jünger, J.
Right arrow Articles by Haass, M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?